Identificación de personas nodales influyentes entre los consumidores de drogas inyectables: un análisis de redes sociales / Identification of Influential Nodal Persons among Injecting Drug Users:- A Social Network Analysis

Autores/as

  • Kabilan Annadurai School of Public Health, SRM University, Chennai, India
  • M Bagavandas School of Public Health, SRM University, Chennai, India

Resumen

Antecedentes: Este estudio se ocupa de los usuarios de drogas inyectables (UDI) que no pudieron realizar el seguimiento del programa de prevención del VIH existente debido a la existencia de estigma. Estos UDI viven en la ciudad de Chennai, en el sur de la India, que es de naturaleza cosmopolita y de mayor tamaño. Objetivo: El objetivo principal de este estudio es identificar al usuario influyente de drogas intravenosas que desempeña un papel activo en la realización de diferentes actividades como comprar y compartir drogas ilícitas, buscar información y asesorar sobre la prevención del VIH mediante el análisis de redes sociales. Métodos: Este estudio cuantitativo transversal se realizó entre los UDI sometidos a Terapia de sustitución de opioides (TSO) durante el período de estudio de abril de 2015 a marzo de 2016 que se incluyeron en el estudio y fueron 46. Como los 46 UDI fueron reclutados como participantes de este estudio, esta red se considera como método de red completa, en el que se estudió la relación existente (Vínculos) entre los UDI. Se utilizó el software de código abierto Node XL para analizar los datos de la red social. Se utilizaron métricas de centralidad del análisis de redes sociales como Grado, Cercanía, Intermediación, Eigen Vector y Page Rank, para identificar los UDI influyentes (personas nodales) dentro de las redes de UDI. Resultados: SNA había identificado al UID-64 como uno de los IDU influyentes (nodo) que es bien conocido por su tráfico de drogas ilícitas. Está bien conectado con otros miembros de UDI en la red, esto se debe a que se comunica con la mayoría de los miembros para distribuir drogas ilícitas y comparte lo mismo con ellos directamente y también a través de otras personas nodales. Este análisis también identificó a UID-39 como una persona de referencia que ha proporcionado información relacionada con el VIH al número máximo de consumidores de drogas inyectables directa e indirectamente, y el UID-67 había motivado a un número máximo de consumidores de drogas inyectables en la red para la prueba del VIH y para inscribirse en el programa OST. . Es sorprendente saber que estas tres influyentes personas nodales no se habían inscrito en el programa OST y eran ex usuarios. Conclusión: El análisis de las redes sociales de los consumidores de drogas inyectables había identificado a los individuos nodales clave que podrían utilizarse para impartir de manera eficaz información esencial sobre la prevención del VIH y para implementar la comunicación conductual. Los hallazgos del estudio destacaron las posibilidades de utilizar métricas de centralidad de la red social como una herramienta para el seguimiento efectivo de los UDI para la prevención del VIH. Palabras clave: OST- Terapia de sustitución de opioides, UDI- Usuario de drogas inyectables, BCC- Comunicación de cambio de comportamiento, UID- Identificación única, PE- Educador de pares y ORW- Trabajador de alcance, NACO- Organización nacional de control del SIDA, IBBS- Biológico y conductual integrados Vigilancia, vigilancia centinela de HSS-VIH. Abstract Background: This study is concerned with Injecting Drug Users (IDUs) who couldn't able to followed up with the existing HIV prevention program due to the existence of stigma. These IDUs are living in Chennai city of South India which is cosmopolitan in nature and the bigger in size. Objective: The primary objective of this study is to identify the influential injecting drug user who plays an active role in carrying out different activities like buying and sharing of illicit drugs, seeking information and advising about HIV prevention using Social Network Analysis Methods: This quantitative cross-sectional study was conducted among IDUs undergoing Opioid Substitution Therapy (OST) during the study period April 2015- March 2016 were included in the study and they were 46 in number. As all the 46 IDUs were recruited as participants of this study, this network is considered as full network method, in which the existing relationship (Ties) among the IDUs was studied. The open source software Node XL was used to analyze the social network data. Centrality metrics of social network analysis like Degree, Closeness, Betweenness, Eigen Vector and Page Rank were used, to identify the influential IDUs (nodal persons) within networks of IDUs. Results: SNA had identified the UID-64 as one of the influential IDU (node) who is well known for his illicit drug dealings. He is well-connected with other IDUs members in the network this is because he communicates with the majority of the members for distributing illicit drugs and shares the same with them directly and also through other nodal persons. This analysis also identified UID-39 as a resource person who has provided HIV-related information to the maximum number of IDUs directly and indirectly and UID-67 had motivated a maximum number of IDUs in the network for HIV testing and to enroll with OST program. It is surprising to know that these three influential nodal persons were themselves had not enrolled with OST Program and were Ex users. Conclusion: Social network analysis of IDUs had identified the key nodal individuals who could be utilized for effectively imparting essential HIV prevention information and implementing behavioral communication. The study findings highlighted the possibilities of utilizing centrality metrics of the social network as a tool for effective follow-up of IDUs for HIV prevention. Key Words: OST- Opioid Substitution Therapy, IDU- Injecting Drug User, BCC- Behavior Change Communication, UID- Unique Identification, PE- Peer Educator and ORW- Out Reach Worker, NACO- National AIDS Control Organization, IBBS- Integrated Biological and Behavioural Surveillance, HSS-HIV Sentinel Surveillance

Biografía del autor/a

Kabilan Annadurai, School of Public Health, SRM University, Chennai, India

School of Public Health, SRM University, Chennai, India

M Bagavandas, School of Public Health, SRM University, Chennai, India

School of Public Health, SRM University, Chennai, India

Citas

Heckathorn D, Magnani R. Snowball and respondent-driven sampling. In: Behavioural Surveillance Surveys: Guidelines for Repeated Behavioural Surveys in Populations at Risk of hIV. ; 2000:1-350. http://www.who.int/hiv/strategic/pubbss/en/index.html

Salganik JM, Heckathornt DD. Sampling and Estimation in Respondent-Driven Sampling. Sociol Methodol. 2004;34(2004):193-239.

Summary E, Key W, Leaders O, Critical A. Identifying Key Opinion Leaders Using Social Network Analysis. Cogniz 20-20 Insights |. 2015;(june).

Haythornthwaite C. Social network analysis: an approach and technique for the study of information exchange. Libr Inf Sci Res. 1996;18(4):323-342. doi:10.1016/S0740-8188(96)90003-1.

Valente TW, Gallaher P, Mouttapa M. Using social networks to understand and prevent substance use: a transdisciplinary perspective. Subst Use Misuse. 2004;39(May):1685-1712. doi:10.1081/LSUM-200033210.

Valente TW, Gallaher P, Mouttapa M. Using social networks to understand and prevent substance use: a transdisciplinary perspective. Subst Use Misuse. 2004;39(May):1685-1712. doi:10.1081/LSUM-200033210.

Margaret R. Weeks et al, (2002), Social Networks of Drug Users in High-Risk Sites: Finding the Connection, Available from: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=B47F816B996F75D12FCE2E6716E75D8F?doi=10.1.1.489.9050&rep=rep1&type=pdf

John E et al (2001) Social Relationships and health:Berkman and Syme (1979),Available from: http://www.psy.cmu.edu/~scohen/berksyme01.pdf

Daniell H. NIH Public Access. 2012;76(October 2009):211-220. doi:10.1007/s11103-011-9767-z.Plastid.

Latkin C, Kuramoto S. Social norms, social networks, and HIV risk behavior among injection drug users. AIDS Behav. 2010;14(5):1159-1168. doi:10.1007/s10461-009-9576-4.Social.

Ilyas MU, Radha H. Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality. Proc 2011 IEEE Int Conf Commun. 2011:1–5. doi:10.1109/icc.2011.5963147.

Bonsignore EM, Dunne C, Rotman D, et al. First steps to NetViz Nirvana: Evaluating social network analysis with NodeXL. Proc - 12th IEEE Int Conf Comput Sci Eng CSE 2009. 2009;4:332-339. doi:10.1109/CSE.2009.120.

Saidel T, Adhikary R, Mainkar M, et al. Baseline integrated behavioural and biological assessment among most at-risk populations in six high-prevalence states of India: design and implementation challenges. AIDS. 2008;22 Suppl 5:S17-S34. doi:10.1097/01.aids.0000343761.77702.04.

Latkin C, Yang C, Srikrishnan AK, et al. The relationship between social network factors, HIV, and Hepatitis C among injection drug users in Chennai, India. Drug Alcohol Depend. 2011;117(1):50-54. doi:10.1016/j.drugalcdep.2011.01.005.

Latkin C, Yang C, Srikrishnan AK, et al. The relationship between social network factors, HIV, and Hepatitis C among injection drug users in Chennai, India. Drug Alcohol Depend. 2011;117(1):50-54. doi:10.1016/j.drugalcdep.2011.01.005.

Solomon SS, Desai M, Srikrishnan AK, et al. NIH Public Access. 2010;45(3):354-367. doi:10.3109/10826080903452447.The.

Panda S, Kumar MS, Lokabiraman S, et al. Risk factors for HIV infection in injection drug users and evidence for onward transmission of HIV to their sexual partners in Chennai, India. J Acquir Immune Defic Syndr. 2005;39(1):9-15. doi:10.1097/01.qai.0000160713.94203.9b.

Panda S, Kumar MS, Lokabiraman S, et al. Risk factors for HIV infection in injection drug users and evidence for onward transmission of HIV to their sexual partners in Chennai, India. J Acquir Immune Defic Syndr. 2005;39(1):9-15. doi:10.1097/01.qai.0000160713.94203.9b.

Panda S, Kumar MS, Lokabiraman S, et al. Risk factors for HIV infection in injection drug users and evidence for onward transmission of HIV to their sexual partners in Chennai, India. J Acquir Immune Defic Syndr. 2005;39(1):9-15. doi:10.1097/01.qai.0000160713.94203.9b.

Koram, Nana et al. “Role of Social Network Dimensions in the Transition to Injection Drug Use: Actions Speak Louder than Words.” AIDS and behavior 15.7 (2011): 1579–1588. PMC Available from 10.1007/s10461-011-9930-1

Descargas

Publicado

2021-02-01

Cómo citar

Annadurai, K., & Bagavandas, M. (2021). Identificación de personas nodales influyentes entre los consumidores de drogas inyectables: un análisis de redes sociales / Identification of Influential Nodal Persons among Injecting Drug Users:- A Social Network Analysis. Medicina Social Social Medicine, 13(3), 161–170. Recuperado a partir de https://socialmedicine.info/index.php/medicinasocial/article/view/1257

Número

Sección

Investigación Original